Emergent data governance: Operationalising big data analytics in state agencies in Kenya

Background: The uptake of big data analytics in the public sector has accelerated, with scholarship positioning data governance as a prerequisite for analytics adoption. Yet, this assumption overlooks realities of many state agencies in developing contexts, where governance structures are weak or evolving, even as analytics systems are used. Consequently, there is limited empirical understanding of how organisations operationalise analytics systems under such conditions. Objectives: This study examines how state agencies navigate analytics implementation when formal data governance structures are weak. Specifically, it investigates how governance practices emerge during analytics operationalisation. Method: Using qualitative research design, data were collected through semi-structured interviews from 30 senior staff from Information Technology, Finance, Strategy, Programmes, Monitoring and Evaluation and Data Analytics across eight state agencies in Kenya. Results: The findings reveal that when analytics adoption begins with weak data governance, agencies develop operational workarounds and informal coordination practices that gradually evolve into more structured governance arrangements. A conceptual model of emergent data governance is proposed to explain this evolutionary process. Conclusion: The study found that weak governance does not prevent analytics adoption. Instead, analytics implementation can itself catalyse the development and institutionalisation of data governance. Contribution: By conceptualising governance of big data analytics as a dynamic rather than static process, the study advances understanding of digital governance and provides practical insights for policymakers seeking to strengthen data governance in the public sector.